Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations66004
Missing cells0
Missing cells (%)0.0%
Duplicate rows4021
Duplicate rows (%)6.1%
Total size in memory25.2 MiB
Average record size in memory400.6 B

Variable types

Text1
Categorical4
Numeric9

Alerts

Dataset has 4021 (6.1%) duplicate rowsDuplicates
birth_state is highly overall correlated with population_2010_x and 9 other fieldsHigh correlation
population_2010_x is highly overall correlated with birth_state and 8 other fieldsHigh correlation
population_2011_x is highly overall correlated with birth_state and 8 other fieldsHigh correlation
population_2012_x is highly overall correlated with birth_state and 8 other fieldsHigh correlation
population_2013_x is highly overall correlated with birth_state and 8 other fieldsHigh correlation
population_2014_x is highly overall correlated with birth_state and 8 other fieldsHigh correlation
population_2015_x is highly overall correlated with birth_state and 8 other fieldsHigh correlation
population_2016 is highly overall correlated with birth_state and 2 other fieldsHigh correlation
population_2017 is highly overall correlated with birth_state and 8 other fieldsHigh correlation
position is highly overall correlated with birth_state and 10 other fieldsHigh correlation
state_name is highly overall correlated with birth_state and 9 other fieldsHigh correlation
team is highly overall correlated with positionHigh correlation
population_2016.1 is highly skewed (γ1 = 38.54508518) Skewed

Reproduction

Analysis started2024-12-11 11:22:57.274114
Analysis finished2024-12-11 11:23:09.756907
Duration12.48 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct115
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-12-11T16:53:10.036836image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length21
Median length18
Mean length13.058193
Min length8

Characters and Unicode

Total characters861893
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowDevin McCourty
2nd rowDevin McCourty
3rd rowDevin McCourty
4th rowDevin McCourty
5th rowDevin McCourty
ValueCountFrequency (%)
johnson 2591
 
1.9%
james 2359
 
1.8%
brandon 1898
 
1.4%
allen 1647
 
1.2%
butler 1515
 
1.1%
joe 1417
 
1.1%
eric 1345
 
1.0%
jones 1324
 
1.0%
vincent 1297
 
1.0%
elliott 1297
 
1.0%
Other values (199) 116964
87.5%
2024-12-11T16:53:10.469512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 85867
 
10.0%
n 71254
 
8.3%
a 70545
 
8.2%
67650
 
7.8%
o 55806
 
6.5%
r 53408
 
6.2%
l 52112
 
6.0%
i 47906
 
5.6%
s 38376
 
4.5%
t 35187
 
4.1%
Other values (41) 283782
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 861893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 85867
 
10.0%
n 71254
 
8.3%
a 70545
 
8.2%
67650
 
7.8%
o 55806
 
6.5%
r 53408
 
6.2%
l 52112
 
6.0%
i 47906
 
5.6%
s 38376
 
4.5%
t 35187
 
4.1%
Other values (41) 283782
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 861893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 85867
 
10.0%
n 71254
 
8.3%
a 70545
 
8.2%
67650
 
7.8%
o 55806
 
6.5%
r 53408
 
6.2%
l 52112
 
6.0%
i 47906
 
5.6%
s 38376
 
4.5%
t 35187
 
4.1%
Other values (41) 283782
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 861893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 85867
 
10.0%
n 71254
 
8.3%
a 70545
 
8.2%
67650
 
7.8%
o 55806
 
6.5%
r 53408
 
6.2%
l 52112
 
6.0%
i 47906
 
5.6%
s 38376
 
4.5%
t 35187
 
4.1%
Other values (41) 283782
32.9%

position
Categorical

High correlation 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
WR
6031 
TE
4446 
T/LOT
 
3719
DT/RDT
 
3697
QB
 
3669
Other values (40)
44442 

Length

Max length11
Median length9
Mean length4.5657687
Min length1

Characters and Unicode

Total characters301359
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS/FS
2nd rowS/FS
3rd rowS/FS
4th rowS/FS
5th rowS/FS

Common Values

ValueCountFrequency (%)
WR 6031
 
9.1%
TE 4446
 
6.7%
T/LOT 3719
 
5.6%
DT/RDT 3697
 
5.6%
QB 3669
 
5.6%
RB 3410
 
5.2%
CB/LCB 2945
 
4.5%
T/ROT 2434
 
3.7%
DE/RDE 2147
 
3.3%
LB/MLB 2021
 
3.1%
Other values (35) 31485
47.7%

Length

2024-12-11T16:53:10.589512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wr 6031
 
9.1%
te 4446
 
6.7%
t/lot 3719
 
5.6%
dt/rdt 3697
 
5.6%
qb 3669
 
5.6%
rb 3410
 
5.2%
cb/lcb 2945
 
4.5%
t/rot 2434
 
3.7%
de/rde 2147
 
3.3%
lb/mlb 2021
 
3.1%
Other values (35) 31485
47.7%

Most occurring characters

ValueCountFrequency (%)
/ 49686
16.5%
R 34088
11.3%
B 33735
11.2%
T 32448
10.8%
L 30632
10.2%
D 23975
8.0%
E 15306
 
5.1%
C 13704
 
4.5%
O 12124
 
4.0%
S 10418
 
3.5%
Other values (8) 45243
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 49686
16.5%
R 34088
11.3%
B 33735
11.2%
T 32448
10.8%
L 30632
10.2%
D 23975
8.0%
E 15306
 
5.1%
C 13704
 
4.5%
O 12124
 
4.0%
S 10418
 
3.5%
Other values (8) 45243
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 49686
16.5%
R 34088
11.3%
B 33735
11.2%
T 32448
10.8%
L 30632
10.2%
D 23975
8.0%
E 15306
 
5.1%
C 13704
 
4.5%
O 12124
 
4.0%
S 10418
 
3.5%
Other values (8) 45243
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 49686
16.5%
R 34088
11.3%
B 33735
11.2%
T 32448
10.8%
L 30632
10.2%
D 23975
8.0%
E 15306
 
5.1%
C 13704
 
4.5%
O 12124
 
4.0%
S 10418
 
3.5%
Other values (8) 45243
15.0%

birth_state
Categorical

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Texas
17038 
Ohio
8415 
California
7230 
Florida
5330 
Illinois
3891 
Other values (27)
24100 

Length

Max length14
Median length11
Mean length7.2703018
Min length4

Characters and Unicode

Total characters479869
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowNew York
5th rowNew York

Common Values

ValueCountFrequency (%)
Texas 17038
25.8%
Ohio 8415
12.7%
California 7230
11.0%
Florida 5330
 
8.1%
Illinois 3891
 
5.9%
Pennsylvania 3042
 
4.6%
New York 3040
 
4.6%
New Jersey 2268
 
3.4%
North Carolina 2212
 
3.4%
Georgia 2152
 
3.3%
Other values (22) 11386
17.3%

Length

2024-12-11T16:53:10.703596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
texas 17038
22.9%
ohio 8415
11.3%
california 7230
9.7%
new 5413
 
7.3%
florida 5330
 
7.2%
illinois 3891
 
5.2%
pennsylvania 3042
 
4.1%
york 3040
 
4.1%
carolina 2752
 
3.7%
jersey 2268
 
3.0%
Other values (24) 16061
21.6%

Most occurring characters

ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 479869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 479869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 479869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

team
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
New England
35650 
Philadelphia
30354 

Length

Max length12
Median length11
Mean length11.459881
Min length11

Characters and Unicode

Total characters756398
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew England
2nd rowNew England
3rd rowNew England
4th rowNew England
5th rowNew England

Common Values

ValueCountFrequency (%)
New England 35650
54.0%
Philadelphia 30354
46.0%

Length

2024-12-11T16:53:10.819602image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T16:53:10.927913image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
new 35650
35.1%
england 35650
35.1%
philadelphia 30354
29.9%

Most occurring characters

ValueCountFrequency (%)
a 96358
12.7%
l 96358
12.7%
n 71300
9.4%
d 66004
8.7%
e 66004
8.7%
h 60708
8.0%
i 60708
8.0%
35650
 
4.7%
w 35650
 
4.7%
N 35650
 
4.7%
Other values (4) 132008
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 756398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 96358
12.7%
l 96358
12.7%
n 71300
9.4%
d 66004
8.7%
e 66004
8.7%
h 60708
8.0%
i 60708
8.0%
35650
 
4.7%
w 35650
 
4.7%
N 35650
 
4.7%
Other values (4) 132008
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 756398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 96358
12.7%
l 96358
12.7%
n 71300
9.4%
d 66004
8.7%
e 66004
8.7%
h 60708
8.0%
i 60708
8.0%
35650
 
4.7%
w 35650
 
4.7%
N 35650
 
4.7%
Other values (4) 132008
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 756398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 96358
12.7%
l 96358
12.7%
n 71300
9.4%
d 66004
8.7%
e 66004
8.7%
h 60708
8.0%
i 60708
8.0%
35650
 
4.7%
w 35650
 
4.7%
N 35650
 
4.7%
Other values (4) 132008
17.5%

population_2016
Real number (ℝ)

High correlation 

Distinct107
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245756.45
Minimum235
Maximum2704958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:11.044955image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum235
5-th percentile4551
Q128350
median72937
Q3256902
95-th percentile947890
Maximum2704958
Range2704723
Interquartile range (IQR)228552

Descriptive statistics

Standard deviation492720.57
Coefficient of variation (CV)2.0049141
Kurtosis14.997631
Mean245756.45
Median Absolute Deviation (MAD)57327
Skewness3.84474
Sum1.6220908 × 1010
Variance2.4277356 × 1011
MonotonicityNot monotonic
2024-12-11T16:53:11.181863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
385809 2805
 
4.2%
25071 1297
 
2.0%
2704958 1297
 
2.0%
15610 1297
 
2.0%
55427 1217
 
1.8%
286057 1217
 
1.8%
108373 1217
 
1.8%
28350 1217
 
1.8%
2303482 1217
 
1.8%
39457 1217
 
1.8%
Other values (97) 52006
78.8%
ValueCountFrequency (%)
235 57
 
0.1%
1026 1217
1.8%
1997 270
 
0.4%
2853 410
 
0.6%
4115 410
 
0.6%
4551 944
1.4%
4731 324
 
0.5%
5084 1014
1.5%
5236 608
0.9%
6391 553
0.8%
ValueCountFrequency (%)
2704958 1297
2.0%
2303482 1217
1.8%
1615017 91
 
0.1%
947890 1217
1.8%
693060 271
 
0.4%
672795 533
0.8%
660388 344
 
0.5%
632912 19
 
< 0.1%
595047 600
0.9%
559277 105
 
0.2%

state_name
Categorical

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Texas
17038 
Ohio
8415 
California
7230 
Florida
5330 
Illinois
3891 
Other values (27)
24100 

Length

Max length14
Median length11
Mean length7.2703018
Min length4

Characters and Unicode

Total characters479869
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowNew York
5th rowNew York

Common Values

ValueCountFrequency (%)
Texas 17038
25.8%
Ohio 8415
12.7%
California 7230
11.0%
Florida 5330
 
8.1%
Illinois 3891
 
5.9%
Pennsylvania 3042
 
4.6%
New York 3040
 
4.6%
New Jersey 2268
 
3.4%
North Carolina 2212
 
3.4%
Georgia 2152
 
3.3%
Other values (22) 11386
17.3%

Length

2024-12-11T16:53:11.309863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
texas 17038
22.9%
ohio 8415
11.3%
california 7230
9.7%
new 5413
 
7.3%
florida 5330
 
7.2%
illinois 3891
 
5.2%
pennsylvania 3042
 
4.1%
york 3040
 
4.1%
carolina 2752
 
3.7%
jersey 2268
 
3.0%
Other values (24) 16061
21.6%

Most occurring characters

ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 479869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 479869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 479869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 62965
13.1%
i 54078
11.3%
o 42034
 
8.8%
e 36658
 
7.6%
s 35387
 
7.4%
n 31832
 
6.6%
l 28071
 
5.8%
r 26799
 
5.6%
T 17726
 
3.7%
x 17143
 
3.6%
Other values (34) 127176
26.5%

population_2010_x
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17603908
Minimum816227
Maximum37327690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:11.418866image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum816227
5-th percentile3050223
Q19712696
median12841196
Q325241648
95-th percentile37327690
Maximum37327690
Range36511463
Interquartile range (IQR)15528952

Descriptive statistics

Standard deviation10050310
Coefficient of variation (CV)0.57091359
Kurtosis-0.69212456
Mean17603908
Median Absolute Deviation (MAD)7053097
Skewness0.45989061
Sum1.1619283 × 1012
Variance1.0100874 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:11.628093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
25241648 17038
25.8%
11539282 8415
12.7%
37327690 7230
11.0%
18846461 5330
 
8.1%
12841196 3891
 
5.9%
12711063 3042
 
4.6%
19405185 3040
 
4.6%
8803708 2268
 
3.4%
9574247 2212
 
3.4%
9712696 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
816227 311
 
0.5%
899712 114
 
0.2%
990507 129
 
0.2%
1363817 2
 
< 0.1%
2064607 105
 
0.2%
2702797 57
 
0.1%
2858403 627
 
0.9%
2970437 894
1.4%
3050223 1888
2.9%
3837073 482
 
0.7%
ValueCountFrequency (%)
37327690 7230
11.0%
25241648 17038
25.8%
19405185 3040
 
4.6%
18846461 5330
 
8.1%
12841196 3891
 
5.9%
12711063 3042
 
4.6%
11539282 8415
12.7%
9876731 1066
 
1.6%
9712696 2152
 
3.3%
9574247 2212
 
3.4%

population_2011_x
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17786455
Minimum823338
Maximum37672654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:11.743096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum823338
5-th percentile3063690
Q19810595
median12862298
Q325644424
95-th percentile37672654
Maximum37672654
Range36849316
Interquartile range (IQR)15833829

Descriptive statistics

Standard deviation10194499
Coefficient of variation (CV)0.57316084
Kurtosis-0.73004829
Mean17786455
Median Absolute Deviation (MAD)7019183
Skewness0.45100702
Sum1.1739772 × 1012
Variance1.0392782 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:11.863098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
25644424 17038
25.8%
11543332 8415
12.7%
37672654 7230
11.0%
19097369 5330
 
8.1%
12862298 3891
 
5.9%
12742811 3042
 
4.6%
19526372 3040
 
4.6%
8844694 2268
 
3.4%
9662940 2212
 
3.4%
9810595 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
823338 311
 
0.5%
907884 114
 
0.2%
996866 129
 
0.2%
1378323 2
 
< 0.1%
2077744 105
 
0.2%
2718170 57
 
0.1%
2868756 627
 
0.9%
2977452 894
1.4%
3063690 1888
2.9%
3865845 482
 
0.7%
ValueCountFrequency (%)
37672654 7230
11.0%
25644424 17038
25.8%
19526372 3040
 
4.6%
19097369 5330
 
8.1%
12862298 3891
 
5.9%
12742811 3042
 
4.6%
11543332 8415
12.7%
9876199 1066
 
1.6%
9810595 2152
 
3.3%
9662940 2212
 
3.4%

population_2012_x
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17975337
Minimum832576
Maximum38019006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:11.980097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum832576
5-th percentile3074386
Q19911171
median12878494
Q326078327
95-th percentile38019006
Maximum38019006
Range37186430
Interquartile range (IQR)16167156

Descriptive statistics

Standard deviation10345755
Coefficient of variation (CV)0.57555278
Kurtosis-0.77125311
Mean17975337
Median Absolute Deviation (MAD)6986814
Skewness0.44147466
Sum1.1864441 × 1012
Variance1.0703465 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:12.102008image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
26078327 17038
25.8%
11546969 8415
12.7%
38019006 7230
11.0%
19341327 5330
 
8.1%
12878494 3891
 
5.9%
12768034 3042
 
4.6%
19625409 3040
 
4.6%
8882095 2268
 
3.4%
9755299 2212
 
3.4%
9911171 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
832576 311
 
0.5%
916868 114
 
0.2%
1003522 129
 
0.2%
1392772 2
 
< 0.1%
2083590 105
 
0.2%
2752410 57
 
0.1%
2885316 627
 
0.9%
2982963 894
1.4%
3074386 1888
2.9%
3893920 482
 
0.7%
ValueCountFrequency (%)
38019006 7230
11.0%
26078327 17038
25.8%
19625409 3040
 
4.6%
19341327 5330
 
8.1%
12878494 3891
 
5.9%
12768034 3042
 
4.6%
11546969 8415
12.7%
9911171 2152
 
3.3%
9886610 1066
 
1.6%
9755299 2212
 
3.4%

population_2013_x
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18153189
Minimum842513
Maximum38347383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:12.240821image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum842513
5-th percentile3089876
Q19981773
median12890403
Q326479279
95-th percentile38347383
Maximum38347383
Range37504870
Interquartile range (IQR)16497506

Descriptive statistics

Standard deviation10487390
Coefficient of variation (CV)0.57771611
Kurtosis-0.80702641
Mean18153189
Median Absolute Deviation (MAD)6957749
Skewness0.43318273
Sum1.1981831 × 1012
Variance1.0998534 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:12.370166image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
26479279 17038
25.8%
11567845 8415
12.7%
38347383 7230
11.0%
19584927 5330
 
8.1%
12890403 3891
 
5.9%
12778450 3042
 
4.6%
19712514 3040
 
4.6%
8913735 2268
 
3.4%
9849812 2212
 
3.4%
9981773 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
842513 311
 
0.5%
925114 114
 
0.2%
1011921 129
 
0.2%
1408038 2
 
< 0.1%
2085161 105
 
0.2%
2786547 57
 
0.1%
2892900 627
 
0.9%
2987721 894
1.4%
3089876 1888
2.9%
3919664 482
 
0.7%
ValueCountFrequency (%)
38347383 7230
11.0%
26479279 17038
25.8%
19712514 3040
 
4.6%
19584927 5330
 
8.1%
12890403 3891
 
5.9%
12778450 3042
 
4.6%
11567845 8415
12.7%
9981773 2152
 
3.3%
9899219 1066
 
1.6%
9849812 2212
 
3.4%

population_2014_x
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18357965
Minimum849455
Maximum38701278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:12.496071image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum849455
5-th percentile3105563
Q110083850
median12882438
Q326954436
95-th percentile38701278
Maximum38701278
Range37851823
Interquartile range (IQR)16870586

Descriptive statistics

Standard deviation10650426
Coefficient of variation (CV)0.58015287
Kurtosis-0.85162923
Mean18357965
Median Absolute Deviation (MAD)7015309
Skewness0.42182571
Sum1.2116991 × 1012
Variance1.1343157 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:12.629789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
26954436 17038
25.8%
11593741 8415
12.7%
38701278 7230
11.0%
19897747 5330
 
8.1%
12882438 3891
 
5.9%
12790341 3042
 
4.6%
19773580 3040
 
4.6%
8943010 2268
 
3.4%
9941160 2212
 
3.4%
10083850 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
849455 311
 
0.5%
934805 114
 
0.2%
1019931 129
 
0.2%
1417710 2
 
< 0.1%
2083207 105
 
0.2%
2831730 57
 
0.1%
2899553 627
 
0.9%
2988578 894
1.4%
3105563 1888
2.9%
3960673 482
 
0.7%
ValueCountFrequency (%)
38701278 7230
11.0%
26954436 17038
25.8%
19897747 5330
 
8.1%
19773580 3040
 
4.6%
12882438 3891
 
5.9%
12790341 3042
 
4.6%
11593741 8415
12.7%
10083850 2152
 
3.3%
9941160 2212
 
3.4%
9914675 1066
 
1.6%

population_2015_x
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18567909
Minimum854036
Maximum39032444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:12.752786image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum854036
5-th percentile3118473
Q110199533
median12862051
Q327454880
95-th percentile39032444
Maximum39032444
Range38178408
Interquartile range (IQR)17255347

Descriptive statistics

Standard deviation10817653
Coefficient of variation (CV)0.58259941
Kurtosis-0.90196392
Mean18567909
Median Absolute Deviation (MAD)7406516
Skewness0.40777946
Sum1.2255562 × 1012
Variance1.1702161 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:12.878786image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
27454880 17038
25.8%
11606027 8415
12.7%
39032444 7230
11.0%
20268567 5330
 
8.1%
12862051 3891
 
5.9%
12791124 3042
 
4.6%
19819347 3040
 
4.6%
8960001 2268
 
3.4%
10041769 2212
 
3.4%
10199533 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
854036 311
 
0.5%
944107 114
 
0.2%
1028317 129
 
0.2%
1426320 2
 
< 0.1%
2082264 105
 
0.2%
2883057 57
 
0.1%
2905789 627
 
0.9%
2985297 894
1.4%
3118473 1888
2.9%
4016537 482
 
0.7%
ValueCountFrequency (%)
39032444 7230
11.0%
27454880 17038
25.8%
20268567 5330
 
8.1%
19819347 3040
 
4.6%
12862051 3891
 
5.9%
12791124 3042
 
4.6%
11606027 8415
12.7%
10199533 2152
 
3.3%
10041769 2212
 
3.4%
9918170 1066
 
1.6%

population_2017
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18932326
Minimum869666
Maximum39536653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:13.000786image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum869666
5-th percentile3145711
Q110429379
median12805537
Q328304596
95-th percentile39536653
Maximum39536653
Range38666987
Interquartile range (IQR)17875217

Descriptive statistics

Standard deviation11086232
Coefficient of variation (CV)0.58557157
Kurtosis-0.98643778
Mean18932326
Median Absolute Deviation (MAD)8178863
Skewness0.38165191
Sum1.2496093 × 1012
Variance1.2290454 × 1014
MonotonicityNot monotonic
2024-12-11T16:53:13.128872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
28304596 17038
25.8%
11658609 8415
12.7%
39536653 7230
11.0%
20984400 5330
 
8.1%
12802023 3891
 
5.9%
12805537 3042
 
4.6%
19849399 3040
 
4.6%
9005644 2268
 
3.4%
10273419 2212
 
3.4%
10429379 2152
 
3.3%
Other values (22) 11386
17.3%
ValueCountFrequency (%)
869666 311
 
0.5%
961939 114
 
0.2%
1050493 129
 
0.2%
1427538 2
 
< 0.1%
2088070 105
 
0.2%
2913123 627
 
0.9%
2984100 894
1.4%
2998039 57
 
0.1%
3145711 1888
2.9%
4142776 482
 
0.7%
ValueCountFrequency (%)
39536653 7230
11.0%
28304596 17038
25.8%
20984400 5330
 
8.1%
19849399 3040
 
4.6%
12805537 3042
 
4.6%
12802023 3891
 
5.9%
11658609 8415
12.7%
10429379 2152
 
3.3%
10273419 2212
 
3.4%
9962311 1066
 
1.6%

population_2016.1
Real number (ℝ)

Skewed 

Distinct7064
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19033.853
Minimum0
Maximum8537673
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size515.8 KiB
2024-12-11T16:53:13.259881image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile158
Q1639
median2241
Q310393
95-th percentile75536
Maximum8537673
Range8537673
Interquartile range (IQR)9754

Descriptive statistics

Standard deviation120622.87
Coefficient of variation (CV)6.3372804
Kurtosis2201.3177
Mean19033.853
Median Absolute Deviation (MAD)1960
Skewness38.545085
Sum1.2563104 × 109
Variance1.4549876 × 1010
MonotonicityNot monotonic
2024-12-11T16:53:13.391330image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 93
 
0.1%
336 81
 
0.1%
199 79
 
0.1%
244 79
 
0.1%
281 75
 
0.1%
230 71
 
0.1%
221 71
 
0.1%
383 70
 
0.1%
122 68
 
0.1%
220 64
 
0.1%
Other values (7054) 65253
98.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 10
 
< 0.1%
6 1
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
10 6
 
< 0.1%
11 25
< 0.1%
ValueCountFrequency (%)
8537673 5
 
< 0.1%
3976322 15
< 0.1%
2704958 3
 
< 0.1%
2303482 14
< 0.1%
1615017 1
 
< 0.1%
1567872 3
 
< 0.1%
1492510 14
< 0.1%
1406630 15
< 0.1%
1317929 14
< 0.1%
1025350 15
< 0.1%

Interactions

2024-12-11T16:53:08.231124image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.035714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.219732image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.528857image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.719379image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.842537image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.000653image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.118249image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.170192image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.329048image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.157926image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.341551image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.648665image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.825381image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.029736image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.110208image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.233835image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.281091image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.434893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.272296image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.563009image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.790404image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.950283image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.173329image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.225249image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.348925image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.396094image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.627257image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.401367image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.708190image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.905332image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.079096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.304771image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.338250image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.462834image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.514278image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.733882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.517246image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.854327image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.022940image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.193097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.420722image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.528854image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.582692image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.632505image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.846411image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.713927image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.984538image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.155801image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.313098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.537868image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.657884image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.705389image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.758514image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.954912image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.847002image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.126832image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.270881image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.438447image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.657946image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.778597image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.830717image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.886740image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:09.063170image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:52:59.971102image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.266074image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.494947image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.584363image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.777864image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:05.897153image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.946852image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.002814image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:09.175177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:00.107605image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:01.411501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:02.616098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:03.732381image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:04.894869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:06.015156image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:07.065854image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-11T16:53:08.121937image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-11T16:53:13.568332image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
birth_statepopulation_2010_xpopulation_2011_xpopulation_2012_xpopulation_2013_xpopulation_2014_xpopulation_2015_xpopulation_2016population_2016.1population_2017positionstate_nameteam
birth_state1.0001.0001.0001.0001.0001.0001.0000.6240.0301.0000.6071.0000.357
population_2010_x1.0001.0001.0001.0001.0000.9970.9970.1140.3010.9950.6991.0000.177
population_2011_x1.0001.0001.0001.0001.0000.9970.9970.1140.3010.9950.6991.0000.177
population_2012_x1.0001.0001.0001.0001.0000.9970.9970.1120.3010.9950.6991.0000.177
population_2013_x1.0001.0001.0001.0001.0000.9970.9970.1120.3010.9950.6991.0000.177
population_2014_x1.0000.9970.9970.9970.9971.0001.0000.1110.3070.9980.6991.0000.177
population_2015_x1.0000.9970.9970.9970.9971.0001.0000.1110.3070.9980.7051.0000.176
population_20160.6240.1140.1140.1120.1120.1110.1111.0000.0730.1100.6500.6240.301
population_2016.10.0300.3010.3010.3010.3010.3070.3070.0731.0000.3070.0000.0300.000
population_20171.0000.9950.9950.9950.9950.9980.9980.1100.3071.0000.7051.0000.176
position0.6070.6990.6990.6990.6990.6990.7050.6500.0000.7051.0000.6070.524
state_name1.0001.0001.0001.0001.0001.0001.0000.6240.0301.0000.6071.0000.357
team0.3570.1770.1770.1770.1770.1770.1760.3010.0000.1760.5240.3571.000

Missing values

2024-12-11T16:53:09.319170image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-11T16:53:09.561816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

player_namepositionbirth_stateteampopulation_2016state_namepopulation_2010_xpopulation_2011_xpopulation_2012_xpopulation_2013_xpopulation_2014_xpopulation_2015_xpopulation_2017population_2016.1
0Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493991769
1Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493991702
2Devin McCourtyS/FSNew YorkNew England18377New York19405185195263721962540919712514197735801981934719849399809
3Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493998856
4Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493992854
5Devin McCourtyS/FSNew YorkNew England18377New York1940518519526372196254091971251419773580198193471984939998111
6Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493995962
7Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493992573
8Devin McCourtyS/FSNew YorkNew England18377New York19405185195263721962540919712514197735801981934719849399499
9Devin McCourtyS/FSNew YorkNew England18377New York194051851952637219625409197125141977358019819347198493991063
player_namepositionbirth_stateteampopulation_2016state_namepopulation_2010_xpopulation_2011_xpopulation_2012_xpopulation_2013_xpopulation_2014_xpopulation_2015_xpopulation_2017population_2016.1
65994Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana4544871457438846026814626795464879746712114684333574
65995Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana45448714574388460268146267954648797467121146843334495
65996Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana45448714574388460268146267954648797467121146843334732
65997Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana4544871457438846026814626795464879746712114684333926
65998Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana45448714574388460268146267954648797467121146843331109
65999Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana454487145743884602681462679546487974671211468433312709
66000Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana454487145743884602681462679546487974671211468433316760
66001Donnie JonesP/HLDLouisianaPhiladelphia227715Louisiana45448714574388460268146267954648797467121146843331950
66002Kamu Grugier-HillLB/SPTM/RLBHawaiiPhiladelphia351792Hawaii1363817137832313927721408038141771014263201427538351792
66003Isaac SeumaloG/ROGHawaiiPhiladelphia351792Hawaii1363817137832313927721408038141771014263201427538351792

Duplicate rows

Most frequently occurring

player_namepositionbirth_stateteampopulation_2016state_namepopulation_2010_xpopulation_2011_xpopulation_2012_xpopulation_2013_xpopulation_2014_xpopulation_2015_xpopulation_2017population_2016.1# duplicates
119Beau AllenDT/RDTMinnesotaPhiladelphia52369Minnesota53107115345967537769554160745452649548323855766061065
636Cole CrostonT/LOGIowaNew England4551Iowa3050223306369030743863089876310556331184733145711815
647Cole CrostonT/LOGIowaNew England4551Iowa30502233063690307438630898763105563311847331457111065
672Cole CrostonT/LOGIowaNew England4551Iowa30502233063690307438630898763105563311847331457111575
949Darren SprolesRBIowaPhiladelphia67934Iowa3050223306369030743863089876310556331184733145711815
960Darren SprolesRBIowaPhiladelphia67934Iowa30502233063690307438630898763105563311847331457111065
985Darren SprolesRBIowaPhiladelphia67934Iowa30502233063690307438630898763105563311847331457111575
21Adam ButlerDT/RDTTexasNew England39457Texas252416482564442426078327264792792695443627454880283045963364
95Beau AllenDT/RDTMinnesotaPhiladelphia52369Minnesota5310711534596753776955416074545264954832385576606444
100Beau AllenDT/RDTMinnesotaPhiladelphia52369Minnesota5310711534596753776955416074545264954832385576606584